Investigating the association of rs7903146 of TCF7L2 gene, rs5219 of KCNJ11 gene, rs10946398 of CDKAL1 gene, and rs9939609 of FTO gene with type 2 diabetes mellitus in Emirati population
School of Medical and Health Sciences
Funding information available at: https://doi.org/10.1016/j.mgene.2019.100600
Background: Type 2 diabetes mellitus is a metabolic condition associated with increased risk of multiple organ complications. Genetic variations in TCF7L2, KCNJ11, CDKAL1, and FTO are shown to be linked to T2DM in many ethnic groups. T. Objective: To investigate the possible associations of these single nucleotide polymorphisms with the susceptibility of T2DM among Emirati population. Methods: The study included 264 unrelated diabetic patients and 153 unrelated healthy controls from Emirati population. DNA was extracted from participants' saliva samples and genotyped for four SNPs rs7903146, rs5219, rs10946398, and rs9939609 using TaqMan® real-Time PCR assays. The associations between the SNPs and T2DM were tested using a multiple logistic regression model incorporating age, gender, body mass index, and hypertension as covariates. Results: A significant association was observed between the genotype distribution of the SNP rs7903146 (TCF7L2) and T2DM (p=.0063, OR=1.80). Further analyses indicated that SNP rs7903146 was possibly interacting with age and BMI to influence the susceptibility to T2DM. In addition, rs5219 (KCNJ11) showed significant link with two anthropometric parameters; BMI (p=.033) and diastolic blood pressure (p=.041), rs10946398 (CDKAL1) affected glycosylated hemoglobin (p=.025) and fasting blood glucose (p=.036), and rs9939609 (FTO) affected BMI (p=.014), systolic blood pressure (p=.044), and fasting blood glucose (p=.034). Conclusions: These findings demonstrate that rs7903146 (TCF7L2) is a risk for T2DM susceptibility among the United Arab Emirates population, while rs5219, rs10946398 and rs9939609 variants may not directly related to T2DM development but to some of its risk factors and related traits.
Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management